Power System Transient Stability Recognition based on Bidirectional Long Short-Term Memory - Fully Connected Neural Networks
Corressponding author's email:
phanvietthinh1978@gmail.comDOI:
https://doi.org/10.54644/jte.2025.1739Keywords:
Transient stability recognition, Power system instability, Deep neural networks, BiLSTM neural networks, Fully connected neural networksAbstract
Fast recognition of power system transient instability is one of the important solutions to prevent power grid collapse. Traditional analysis methods are slow in making control decisions, and simulation methods require much time and are not feasible, neural networks overcome this drawback because they calculate quickly and accurately. This paper introduces the application of BiLSTM-FC (Bidirectional Long Short-Term Memory - Fully Connected) deep neural network architecture to identify the transient stability of power systems, and it applies a confusion matrix to test the recognition accuracy of each layer. Simulations to determine stable or unstable power systems are performed on IEEE 39bus power systems with the help of PowerWorld software to create a network training database. The test results comparing the performance between BiLSTM-FC and BiLSTM architectures show that BiLSTM-FC architecture achieves better performance than BiLSTM architecture. The BiLSTM-FC has a validation accuracy as high as 99.5%. Compared with BiLSTM, BiLSTM-FC has 2.77% higher validation accuracy.
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